This invention relates to browser proxies that transform image objects embedded inside documents. It is more generally related to any proxy or gateway system that processes MIME encapsulated objects and the the world wide web.
Transcoding proxies sometimes act as intermediaries between web servers and a variety of client devices that are connected over communication links with widely varying characteristics. Generally, a transcoding proxy provides two major benefits: dramatic reduction in download times for low-bandwidth links, and tailoring of web data to the client device.
Mobile devices are frequently connected via low-bandwidth wireless or medium bandwidth wireline modem links that make the viewing of rich web content, such as images, very cumbersome due to the very long download times that result. In addition, the cost of such downloads can be prohibitive over tariffed wide-area wireless networks. Transcoding web proxies can reduce the size of web data while maintaining most of its semantic value. Download time reductions of six to ten times may be achieved for many typical web images without losing their intelligibility.
The second important benefit is that such an intermediate proxy is sometimes also capable of tailoring text and images for the multitude of small, weakly connected, but web-enabled, mobile devices that are now available. The capabilities of these mobile devices to receive, process, store and display web content varies widely. Given the variety of client devices, it is difficult for Internet content publishers to tailor content for the individual devices. Consequently, an active web proxy is used to transcode/change the web content to best fit the resolution, color-depth, and size constraints of a small-screened device and to reduce the size of the stored data to a fraction of its original size.
A typical transcoding web proxy may be implemented by adding a transcoding module in an HTTP proxy. The transcoding module is an input/output system that takes a data object (e.g., an image or HTML page) as input and transforms it into a low resolution object (e.g., a low-quality image or summarized HTML page) according to a set of chosen parameters. These parameters are input to the transcoder, which determines the resolution loss and size reduction of the object. The following discussion is mostly concerned with image objects.
To maximize the benefits of transcoding, it is important to choose the quality Vs. size tradeoff such that the best quality image is sent within the delay tolerance specified by the user. Choosing the right set of transcoding parameters, however, is difficult. First, it is hard to estimate the size of the transcoded image size because the degree of compression achieved through transcoding is content dependent. For example, some JPEG files can be compressed by up to 80% while others may only yield less than 5% compression. The same is true when GIF-to-GIF or GIF-to-JPEG transformations are applied. Since transcoding is a compute-intensive process, processing is only worthwhile when high compression ratios can be achieved. For images that are already well-compressed (or of low quality) transcoding may be wasteful.
Even if the size of the transcoded output can be predicted, network variability makes it hard to predict image download times. Since the user perceived quality and latency are the final performance measures, accurate estimates of download times are needed in order to choose the optimal point in the quality Vs. size tradeoff. Over best effort service networks, such as the Internet, accurate estimates of network characteristics (bandwidth, delay, loss rate) are hard to compute. Often the variance is so large that the statistical estimates are not meaningful for making any quality adjustments.
Another issue that makes policy decisions a complicated task is the location of the bottleneck in the network. When the path between the web server and the proxy is the bottleneck, transcoding does not help at all. On the contrary, it makes things worse due to the store and forward nature of the operation. Ideally, web proxies should stream images from web servers to clients whenever the server to proxy path is the bottleneck. Even in cases where the proxy to client path is the bottleneck, any benefit due to size reduction must be weighed against the store and forward delay incurred due to transcoding.
Due to the difficulty of estimating the network characteristics and image content, transcoding proxies may often only support adaptation at a very coarse level. Adaptation involves changing transcoding parameters which are usually selected from a predefined set of defaults, or chosen by the user. The problem with both approaches is that the full potential of transcoding is not utilized. Predefined static policies are unresponsive to changes in network bandwidth and user selectable policy decisions are likely to be sub-optimal due to the lack of knowledge about the state of the network. No method exists for providing feedback to the user about the combined state of the network and the proxy. Using iterative refinement, users can converge to the optimal choice of transcoding parameters, but such a process is time consuming and not very user-friendly.
FIG. 1 shows an example transcoding proxy scenario 100 with varied links and varied client devices. As shown in FIG. 1, we consider an example scenario in which all of the web requests and responses for clients 130-134 are passed through a generic (Hypertext Transfer Protocol) HTTP proxy 190. In the example scenario of FIG. 1, it is the downloading of large data objects, primarily images, over the last link 160-164 from the proxy to the client that typically is the main source of delay in the end-to-end response time experienced by the clients 130-134.
The transcoding proxy 190 provides massive data thinning of web data to enable real-time browsing of web data over low-speed wide-area wireless links, like Cellular Digital Packet Data (CDPD) 163 that provides 10 kb/s or less of throughput, depending upon the number of clients sharing the link. The end client could be a full-function PC 134 with a good color display, so that the primary problem is bandwidth reduction. Alternatively, the client might be a small, web-enabled, mobile devices 130-132 in which case it is advantageous to tailor the web data for the specific client device, particularly the client""s display characteristics. Different aspects of transcoding proxy design benefit different scenarios (i.e., tariffed proxy-client link, strong/weak client display, etc.), but all are handled well within the same proxy architecture 100.
FIG. 2 shows a block diagram of an embodiment of transcoding proxy 190 used to transform objects based on user specified preferences and static policies. A transcoding proxy 190 is built by combining a transcoding module 240 with an HTTP proxy engine 220. An HTTP request 222 originates from a client 230 and is forwarded 224 by the proxy 190 to a web server 210. The response data 226 (i.e., HTML pages and GIF and JPEG images) are transformed by the transcoder 240 and then forwarded 228 to the client 230. Typically, a number of transcoding parameters are specified to the transcoder 240 in order to achieve the desired quality/size reduction of the object contained in the response data 226. Transcoding proxies in use today either use a static set of policies 250 or use some form of user specified preferences 260 via path 265 to determine the transcoding parameters. When a fixed set of transcoding parameters are applied to all objects, results are not always beneficial. In fact, in many cases, transcoding leads to poorer performance.
An aspect of the present invention is to provide methods, devices and systems for dynamically adjusting transcoding parameters so as to increase the benefits of transcoding. Methods of adaptation are designed to cope with the variability of network characteristics and of the size of transcoded images.
In an embodiment, the invention includes three new components: an image size predictor, a network bandwidth (b/w) analyzer, and a policy module. Before initiating any transcoding action, the policy module queries the image size predictor to estimate the size of the output image. The b/w analyzer is queried to collect an estimate of the image transmission time from the server to the proxy, and from the proxy to the client. Based on the collected estimates, the proxy decides whether or not to transcode the image. In addition, the policy module can also compute the optimal point in the quality vs. size tradeoff that would provide the user specified performance criteria (e.g., reduced response time, increased quality).
Another aspect of the present invention is to provide a method and/or apparatus to enable the transcoding proxy to adjust a quality-size tradeoff on a per-image and/or a per-client basis. The adaptive transcoder chooses different parameters for each object, and provides performance improvements.
Still another aspect of this invention provides a general framework for making policy decisions taking into account available bandwidth, content and type of image, and user preferences. The administrator of the proxy chooses from a variety of optimization objectives so as to obtain improved performance from the system. In one embodiment when the proxy transcoder throughput is the bottleneck, the policy module is instructed to judiciously use CPU resources so as to reduce the response time for all users. An advantageous element of the invention is the automated nature of decision making, to free up users from actively controlling the policy engine of the proxy.
In still another aspect of the present invention a method is provided for generating feedback about the choice of optimal transcoding parameters to the user. In an example of embodiment, the transcoding system provides feedback to the user by dynamically adjusting the position of the user preference slider bar. The user preference slider bar serves both as an input as well as an output device.